Biology subjects

Roshchupkin, G.

Publications and source records attributed to Roshchupkin, G..

3 recordsLinked to original sources

bioRxiv2019

Grey Matter Age Prediction as a Biomarker for Risk of Dementia: A Population-based Study

Key PointsO_ST_ABSQuestionC_ST_ABSIs the gap between brain age predicted from MRI and chronological age associated with incident dementia in a general population of Dutch adults?\n\nFindingsBrain age was predicted using a deep learning model, using MRI-derived grey matter density maps. In a population based study including 5496 participants, the observed gap was significantly associated with the risk of dementia.\n\nMeaningThe gap between MRI-brain predicted and chronological age is potentially a biomarker for dementia risk screening.\n\nAbstractO_ST_ABSImportanceC_ST_ABSThe gap between predicted brain age using magnetic resonance imaging (MRI) and chronological age may serve as biomarker for early-stage neurodegeneration and potentially as a risk indicator for dementia. However, owing to the lack of large longitudinal studies, it has been challenging to validate this link.\n\nObjectiveWe aimed to investigate the utility of such a gap as a risk biomarker for incident dementia in a general Dutch population, using a deep learning approach for predicting brain age based on MRI-derived grey matter maps.\n\nDesignData was collected from participants of the cohort-based Rotterdam Study who underwent brain magnetic resonance imaging between 2006 and 2015. This study was performed in a longitudinal setting and all participant were followed up for incident dementia until 2016.\n\nSettingThe Rotterdam Study is a prospective population-based study, initiated in 1990 in the suburb Ommoord of in Rotterdam, the Netherlands.\n\nParticipantsAt baseline, 5496 dementia- and stroke-free participants (mean age 64.67{+/-}9.82, 54.73% women) were scanned and screened for incident dementia. During 6.66{+/-}2.46 years of follow-up, 159 people developed dementia.\n\nMain outcomes and measuresWe built a convolutional neural network (CNN) model to predict brain age based on its MRI. Model prediction performance was measured in mean absolute error (MAE). Reproducibility of prediction was tested using the intraclass correlation coefficient (ICC) computed on a subset of 80 subjects. Logistic regressions and Cox proportional hazards were used to assess the association of the age gap with incident dementia, adjusted for years of education, ApoE{varepsilon}4 allele carriership, grey matter volume and intracranial volume. Additionally, we computed the attention maps of CNN, which shows which brain regions are important for age prediction.\n\nResultsMAE of brain age prediction was 4.45{+/-}3.59 years and ICC was 0.97 (95% confidence interval CI=0.96-0.98). Logistic regression and Cox proportional hazards models showed that the age gap was significantly related to incident dementia (odds ratio OR=1.11 and 95% confidence intervals CI=1.05-1.16; hazard ratio HR=1.11 and 95% CI=1.06-1.15, respectively). Attention maps indicated that grey matter density around the amygdalae and hippocampi primarily drive the age estimation.\n\nConclusion and relevanceWe show that the gap between predicted and chronological brain age is a biomarker associated with risk of dementia development. This suggests that it can be used as a biomarker, complimentary to those that are known, for dementia risk screening.

epidemiology↗

bioRxiv2018

High-dimensional mapping of cognition to the brain using voxel-based morphometry and subcortical shape analysis.

BackgroundIt is increasingly recognized that the complex functions of human cognition are not accurately represented by arbitrarily-defined anatomical brain regions. Given the considerable functional specialization within such regions, more fine-grained studies of brain structure could capture such localized associations. However, such analyses/studies in a large community-dwelling population are lacking.\n\nMethodsIn 3,813 stroke-free and non-demented persons from the Rotterdam Study (mean age 69.1 ({+/-}8.8) years; 55.8% women) with cognitive assessments and brain MRI, we performed voxel-based morphometry and subcortical shape analysis on global cognition and separate tests that tapped into memory, information processing speed, fine motor speed, and executive function domains.\n\nResultsWe found that the different cognitive tests significantly associated with grey matter voxels in differential but also overlapping brain regions, primarily in the left hemisphere. Clusters of significantly associated voxels with global cognition were located within multiple anatomic regions: left amygdala, hippocampus, parietal lobule, superior temporal gyrus, insula and posterior temporal lobe. Subcortical shape analysis revealed associations primarily within the head and tail of the caudate nucleus, putamen, ventral part of the thalamus, and nucleus accumbens, more equally distributed among the left and right hemisphere. Within the caudate nucleus both positive (head) as well as negative (tail) associations were observed with global cognition.\n\nConclusionsIn a large population-based sample, we mapped cognitive performance to (sub)cortical grey matter using a hypothesis-free approach with high-dimensional neuroimaging. Leveraging the power of our large sample size, we confirmed well-known associations as well as identified novel brain regions related to cognition.

neuroscience↗

bioRxiv2018

Planar cell polarity pathway and development of the human visual cortex

The radial unit hypothesis provides a framework for global (proliferation) and regional (distribution) expansion of the primate cerebral cortex. Using principal component analysis (PCA), we have identified cortical regions with shared variance in their surface area and cortical thickness, respectively, segmented from magnetic resonance images obtained in 23,800 participants. We then carried out meta-analyses of genome-wide association studies of the first two principal components for each phenotype. For surface area (but not cortical thickness), we have detected strong associations between each of the components and single nucleotide polymorphisms in a number of gene loci. The first (global) component was associated mainly with loci on chromosome 17 (9.5e-32 [≤] p [≤] 2.8e-10), including those detected previously as linked with intracranial volume and/or general cognitive function. The second (regional) component captured shared variation in the surface area of the primary and adjacent secondary visual cortices and showed a robust association with polymorphisms in a locus on chromosome 14 containing Disheveled Associated Activator of Morphogenesis 1 (DAAM1; p=2.4e-34). DAAM1 is a key component in the planar-cell-polarity signaling pathway. In follow-up studies, we have focused on the latter finding and established that: (1) DAAM1 is highly expressed between 12th and 22nd post-conception weeks in the human cerebral cortex; (2) genes co-expressed with DAAM1 in the primary visual cortex are enriched in mitochondria-related pathways; and (3) volume of the lateral geniculate nucleus, which projects to regions of the visual cortex staining for cytochrome oxidase (a mitochondrial enzyme), correlates with the surface area of the visual cortex in major-allele homozygotes but not in carriers of the minor allele. Altogether, we speculate that, in concert with thalamocortical input to cortical subplate, DAAM1 enables migration of neurons to cytochrome-oxidase rich regions of the visual cortex, and, in turn, facilitates regional expansion of this set of cortical regions during development.

neuroscience↗